Particle filter based positioning with 3GPP-LTE in indoor environments

Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, especially in urban and indoor scenarios with severe multipath propagation and blocking of satellites by buildings the accuracy loss can be very large. Often, a position with GNSS is impossible in these scenarios. On the other hand, cellular wireless communication systems such as the third generation partnership project (3GPP) long-term evolution (LTE) provide excellent coverage in urban and most indoor environments. Thus, this paper researches timing based positioning algorithms, in this case time difference of arrival (TDoA), using 3GPP-LTE measurements. Several approaches and algorithms exist to solve the navigation equation for cellular systems, for instance Bayes filtering methods such as Kalman or particle filter. This paper specifically considers and develops a particle filter for 3GPP-LTE TDoA positioning. To obtain better positioning results, a 3GPP-LTE TDoA error model is derived. This error model is afterwards included in the likelihood function of the particle filter. The last part of this paper, evaluates the positioning performances of the developed particle filter in an indoor scenario. These evaluations show clearly the possibility of using 3GPP-LTE measurements for indoor positioning.